SeaRum: A Cloud-Based Service for Association Rule Mining

Data Integration and Mining based on Web Big Data

“…More and more scholars are beginning to pay attention to the efficiency and highly-accurate processing of the social network analysis [13]. Xiong Zhengli and some other people proposed a discovery algorithm based on the users of online social network community according to the unique characteristics of online social network and aiming at the hard-detected problems of potential communities for online social networks [14]. Fang Ping and some other people proposed a new discovery algorithm of community structure based on the number of common friends and neighbor nodes information.…”

Misleading Generalized Itemset Mining in the Cloud

“…The goal is to propose itemset extraction algorithms that distribute data and computation across a distributed architecture to scale the mining process towards Big Data [3], [4], [5]. A parallel version of an established itemset mining algorithm, i.e., FP-Growth, has first been proposed in [3].…”

“…Specifically, Dist-Eclat focuses on improving algorithm speed, while BigFIM is optimized to run on huge datasets. In parallel, a cloud-based service for association rule mining from network traffic data has been presented in [5]. Unlike [3], [4], [5], this paper investigates the applicability of a generalized pattern mining technique on the MapReduce platform.…”

“…Itemset extraction. To efficiently mine frequent levelsharing itemsets [13] from the extended dataset version, this task exploits a variation of the Hadoop-based itemset mining algorithm proposed in [5].…”

“…Data mining encompasses a large variety of techniques, such as cluster analysis, frequent pattern extraction, and classification [2]. For example, some remarkable attempts to address itemset and association rule mining from Big Data have recently been made (e.g., [3], [4], [5], [6]). This paper…”